Chapter 15 – The Epigenetics of Suicide: The Critical Impact of the Environment on Epigenetic Regulation in Suicide
Bibliographic record
Abstract
Suicide is a problem resulting from an interaction among several factors. Among these factors, early-life adversity, characterized by child sexual and physical abuse as well as parental neglect, is one of the strongest risk factors for depression and suicidal behaviors. Although it is clear that child abuse increases the risk for depression and suicide, the mechanisms mediating these effects are still unknown. Recent evidence suggests that epigenetic mechanisms may be involved in mediating the effects of early-life adversity on behavior. Early-life adversity in humans and animals has been shown to alter DNA methylation in genes regulatory regions which, in turn, has been associated with changes in gene expression and behavioral modifications. However, although epigenetic modifications have been found in several genes, the extent of epigenetic changes induced by early-life adversity is still unknown and their impacts on increasing suicide risk are unclear. This chapter aims at identifying and describing the molecular mechanisms by which early-life adversity induces behavioral changes conferring vulnerability toward mood disorders and suicidal behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".